Svalorzen/AI-Toolbox

A C++ framework for MDPs and POMDPs with Python bindings

48
/ 100
Emerging

This C++ framework, with Python access, helps AI/ML researchers and practitioners create and solve complex decision-making problems. It takes in descriptions of environments where actions lead to uncertain outcomes and rewards, such as a partially observable system, and outputs optimal strategies or policies. It's designed for those developing or applying reinforcement learning and planning algorithms.

669 stars. No commits in the last 6 months.

Use this if you are developing or implementing reinforcement learning algorithms for systems with uncertain outcomes and observations, such as robotics, game AI, or resource management.

Not ideal if you need a high-level, off-the-shelf solution for simple predictive modeling or if you are not comfortable with programming concepts related to algorithm implementation.

reinforcement-learning decision-making-under-uncertainty planning-algorithms robotics-control stochastic-systems
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 22 / 25

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Stars

669

Forks

102

Language

C++

License

GPL-3.0

Last pushed

Mar 18, 2025

Commits (30d)

0

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